Energy flow optimization method for multi-energy system oriented to combined cooling, heating and power

Combined cooling, heating, and power (CCHP) systems use waste heat from on-site electricity generation to meet the thermal demand of the facility, which includes cooling, heating, and hot water. When compared with traditional set-ups of grid-generated electricity and an on-site boiler for heat, CCHP systems are capable of improving fuel utilization and lowering emissions. One of the critical components affecting the performance of CCHP systems is the prime mover. The objective of this investigation is to study the effect of the prime mover size and operational strategy on the energy, economical, and environmental performance of CCHP systems under different pricing structures. Three different sizes for the prime mover, a natural gas engine, are simulated under different operational strategies such as: following the electric demand of the facility, following the thermal demand of the facility, and following a constant load. The results obtained for the CCHP system will be compared with a reference building operating under conventional technologies to determine the advantages or disadvantages of the CCHP system's operation. In addition, a simple optimization of the system's operation determines the best engine load for each hour during the simulation that minimizes cost, primary energy consumption (PEC), or carbon dioxide emissions (CDEs). For this study, three locations in different climate zones that have different electricity rate structures were chosen: one with a constant flat rate; one that uses seasonal rates; and one that incorporates block charges. Historical monthly natural gas rates were also used. Since most authors perform cost analyses using a fixed constant rate for both electricity and natural gas, a comparison is made between the cost results from using the actual cost data to those that use an average fixed rate. In general, among the three engine sizes that were simulated, the smallest engine yielded the lowest costs and lowest PEC; but, no such trend was found with regard to the CDE. The results also showed that the performance of the evaluated CCHP system can be improved by optimizing the system based on cost or primary energy.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Novel performance curves to determine optimal operation of CCHP systems

A probability constrained multi-objective optimization model for CCHP system operation decision support

In this paper,a combined cooling heating and power (CCHP) system model considering energy supply pipe network is proposed. Compared with the traditional CCHP system framework, more types of energy supply equipment and their possible connections are considered during the stage of energy supply station modeling. During operation simulation, energy supply regulation is applied according to daily load profile. Heat loss and pressure loss of the pipe network are also calculated in the process. A Particle Swarm Optimization (PSO) - Iteration (IM) - Interior point (LP) optimization algorithm is applied to the combined planning of energy station and energy supply network of CCHP Micro-system. By this method, the influence of energy supply pipe network model on CCHP Micro-system design and planning, as well as economic efficiency of the whole project are studied.

Multi-objective capacity optimization of a hybrid energy system in two-stage stochastic programming framework

Design and simulation of gas turbine-based CCHP combined with solar and compressed air energy storage in a hotel building

Energy management and economics of a trigeneration system Considering the effect of solar PV, solar collector and fuel price

This paper proposes an optimization method based on Multi-Objective Particle Swarm Optimization (MOPSO) for addressing the operational optimization challenges of Combined Cooling, Heating, and Power (CCHP) integrated energy systems. CCHP systems enhance energy efficiency and reduce environmental pollution, thus possessing significant economic and environmental benefits. Despite existing research progress, current methodologies still inadequately address the simultaneous optimization of economic and environmental aspects. The key contributions of this research include constructing an operational optimization model for the CCHP system, improving the MOPSO algorithm, specifically in terms of inertia weight, learning factors, and individual optimal values, and applying these improvements to solve the model. The theoretical foundations of the CCHP system, multi-objective optimization problems, principles of Particle Swarm Optimization (PSO), and the characteristics and advantages of MOPSO are discussed comprehensively. The optimization model targets minimizing economic costs and optimizing environmental performance, clearly defining decision variables and constraints, and rigorously evaluating MOPSO algorithm applicability. A detailed procedure for constructing and solving the optimization model is provided. A case study is conducted by establishing background information, setting system parameters, configuring MOPSO algorithm parameters, and performing the optimization. Results are thoroughly analyzed, comparing the method's effectiveness against other optimization methods to validate its superiority. The study concludes that this approach effectively optimizes CCHP operations, providing a reference for coordinated planning in integrated energy systems, and discusses future research directions.

Grid aided combined heat and power generation system for rural village in north China plain using improved PSO algorithm

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

Thermodynamic analysis of fuel cell combined cooling heating and power system integrated with solar reforming of natural gas

Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties

Integration of Photovoltaic Technology to Decentralized Liquefied Natural Gas-Fueled Combined-Cooling-Heating-and-Power Plants

• Methodology for exergy analysis of bio-solar CCHP/HP systems. • Transient and dynamic model for optimal sizing of gasifier and other components. • Optimal sizing can significantly reduce irreversibility losses. • Biomass types have marginal effect on system exergy efficiency. Biomass-based, small-scale Combined Cooling, Heating and Power (CCHP) systems can increase the energy autonomy of building complexes while reducing their Greenhouse Gases (GHG) emissions. In combination with air-to-water Heat Pumps (HP), highly efficient energy systems can be designed, which allow for high flexibility in serving thermal and electric demands. This study presents an exergy evaluation of such a small-scale CCHP/HP system using a dynamic modelling approach based on gasification of various types of woody biomass. The transient model takes into account the effects of the biomass chemical composition as well as of ambient temperatures on the exergy performance of all key components for various CCHP system sizes supporting an HP system. The model has been applied in a case study for a historic building complex, where the CCHP sub-system would support an HP-subsystem allowing for downsizing the latter. The results show that smaller CCHP systems operate with higher exergy efficiency, because for larger systems the heat transfer from the engine and the operation of the absorption chiller are less exergy efficient. The electric load-following CCHP subsystem reaches exergy efficiencies of up to 13.3%, indicating huge improvement potential for system and sub-system design. It was also found that the biomass composition heavily influences the exergy efficiency of the gasifier and in turn changes the exergy efficiency of the syngas engine and the entire CCHP sub-system.